Centroid for improving machine learning classification and info retrieval

ABSTRACT

Centroids are used for improving machine learning classification and information retrieval. A plurality of files are classified as malicious or not malicious based on a function dividing a coordinate space into at least a first portion and a second portion such that the first portion includes a first subset of the plurality of files classified as malicious. One or more first geometric regions are defined in the first portion that classify files from the first subset as not malicious. A file is determined to be malicious based on whether the file is located within the one or more first geometric regions.

RELATED APPLICATION

The current application claims priority to U.S. Pat. App. Ser. No. 62/402,842 filed on Sep. 30, 2016, the contents of which are hereby fully incorporated by reference.

TECHNICAL FIELD

The subject matter described herein relates generally to machine learning and more specifically to the use of centroids for improving machine learning classification and information retrieval (also referred to herein as “info” retrieval).

BACKGROUND

In order to detect malicious software (e.g., viruses, worms, Trojan horses, spyware, and/or programming that gathers information about a computer/user without permission), computer systems may utilize file classification systems to identify files with certain characteristics which make it likely that the files are malicious. Although simple file classification systems may be computationally conservative, they may be over-inclusive and improperly classify clean files as malicious. Similarly, simple file classification systems may be under-inclusive, and improperly classify malicious files as clean. Thus, systems with a greater ability to detect and classify malicious and/or clean files can be desirable.

SUMMARY

Systems, methods, and articles of manufacture, including computer program products, are provided for use of centroids for classification and information retrieval. In some example embodiments, there is provided a system that includes at least one processor and at least one memory including program code which when executed by the at least one memory provides operations. The operations can include classifying a plurality of files as malicious or not malicious based on a function dividing a coordinate space into at least a first portion and a second portion, wherein the first portion includes a first subset of the plurality of files classified as malicious. The operations can further include defining one or more first geometric regions in the first portion that classifies files from the first subset as not malicious. The operations can further include determining whether a file is malicious based on whether the file is located within the one or more first geometric regions.

In some variations, one or more features disclosed herein including the following features can optionally be included in any feasible combination. In some embodiments, the second portion can include a second subset of the plurality of files classified as not malicious, and/or the operations can further comprise defining one or more second geometric regions in the second portion that classifies files from the second subset as malicious. In accordance with these embodiments, determining whether the file is malicious can further comprise determining whether the file is located within a region of the second portion that does not include the one or more second geometric regions. If the file is located within the one or more first geometric regions the file may be classified as not malicious. Similarly, if the file is located within a region of the second portion that does not include the one or more second geometric regions, the file may be classified as not malicious.

In various embodiments, the operations can further comprise determining a plurality of attributes of the plurality of files, and/or mapping the plurality of files in a positive portion of the coordinate space defined by an intersection of at least two of the plurality of attributes. In some embodiments, the operations can further comprise identifying a plurality of clusters from the plurality of files, determining whether any of the plurality of clusters do not include known malicious files, and/or defining individual geometric regions around each of the plurality of clusters which do not include known malicious files, wherein the one or more first geometric regions includes the individual geometric regions. In related embodiments, the operations can further comprise determining whether any of the individual geometric regions include a radius greater than a threshold value, reducing the radius of the individual geometric regions which are greater than the threshold value such that the radius is less than or equal to the threshold value, and/or re-defining (e.g., after the reducing) the individual geometric regions which no longer include all files from a respective cluster of the plurality of clusters, where the re-defining includes defining multiple smaller geometric regions in place of the individual geometric regions.

In some embodiments, the one or more first geometric regions can include a circular geometry having a center point and a radius, and the file can be determined to be located within the one or more first geometric regions when a distance between the center point and a location of the file is less than or equal to the radius. In some aspects, the center point can be determined based on averaging locations for each of the plurality of files located within the one or more first geometric regions. In some aspects, the center point can be determined based on shared attributes for each of the plurality of files located within the one or more first geometric regions. In some aspects, the radius can be determined based on a maximum Euclidian distance between each of the plurality of files located within the one or more first geometric regions.

Additional systems, methods, and articles of manufacture, including computer program products, are provided for use of centroids for classification and information retrieval. In some example embodiments, there is provided a system that includes at least one processor and at least one memory including program code which when executed by the at least one memory provides operations. The operations can include searching for one or more clusters among a plurality of files in a coordinate space. The operations can further include defining one or more geometric regions around the one or more clusters, the one or more geometric regions classifying a set of files within the one or more geometric regions as belonging to a specific classification type. The operations can further include classifying a file as belonging to the specific classification type when the file is located within the one or more first geometric regions.

In some variations, one or more features disclosed herein including the following features can optionally be included in any feasible combination. In some embodiments, the specific classification type includes one or more of safe, suspect, benign, unsafe, malicious, non-malicious, downloader, Trojan, and adware. In some embodiments, the operations can further comprise determining a plurality of attributes of the plurality of files, and/or mapping the plurality of files in the coordinate space, wherein the coordinate space is defined by an intersection of at least two of the plurality of attributes.

In various embodiments, the one or more first geometric regions include a circular geometry having a center point and a radius. In related embodiments, the operations can further comprise determining whether the file is located within the one or more first geometric regions based on whether a distance between the center point and a location of the file is less than or equal to the radius. In some aspects, the center point can be determined based on averaging locations for each of the plurality of files located within the one or more first geometric regions. In some aspects, the center point can be determined based on shared attributes for each of the plurality of files located within the one or more first geometric regions. In some aspects, the radius can be determined based on a maximum Euclidian distance between each of the plurality of files located within the one or more first geometric regions.

Implementations of the current subject matter can include, but are not limited to, methods consistent with the descriptions provided herein as well as articles that comprise a tangibly embodied machine-readable medium operable to cause one or more machines (e.g., computers, etc.) to result in operations implementing one or more of the described features. Similarly, computer systems are also described that may include one or more processors and one or more memories coupled to the one or more processors. A memory, which can include a non-transitory computer-readable or machine-readable storage medium, may include, encode, store, or the like one or more programs that cause one or more processors to perform one or more of the operations described herein. Computer implemented methods consistent with one or more implementations of the current subject matter can be implemented by one or more data processors residing in a single computing system or multiple computing systems. Such multiple computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g. the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc.

The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. While certain features of the currently disclosed subject matter are described for illustrative purposes, it should be readily understood that such features are not intended to be limiting. The claims that follow this disclosure are intended to define the scope of the protected subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and, together with the description, help explain some of the principles associated with the disclosed implementations. In the drawings,

FIG. 1 illustrates an example of a system in which file classification and information retrieval solutions may be implemented, in accordance with some embodiments;

FIG. 2 illustrates an example mapping of a plurality of files in space, in accordance with some embodiments;

FIG. 3 illustrates an example mapping of a plurality of files in space, in accordance with some embodiments;

FIG. 4 illustrates an example of a mapping including a centroid, in accordance with some embodiments;

FIG. 5 illustrates another example of the mapping of FIG. 4 including multiple centroids, in accordance with some embodiments;

FIG. 6 illustrates an example of a mapping including a plurality of centroids, in accordance with some embodiments;

FIG. 7 illustrates an example of a computing apparatus, in accordance with various embodiments;

FIG. 8 illustrates an example of a method for searching for files, in accordance with various embodiments; and

FIG. 9 illustrates another example of a method for searching for files, in accordance with various embodiments.

DETAILED DESCRIPTION

Users run software programs with a variety of capabilities on endpoints. An endpoint can include one or more of a personal computer, a smart phone, a tablet, and/or the like. Certain software programs and/or files can include malicious code (referred to herein as “malware”), which can be harmful to the user and/or the endpoint. Therefore, in order to protect themselves from malware, users can install or otherwise utilize malware detection software on or through the endpoint.

Computer programs are typically compiled to generate executable programs. The compilation process transforms the computer program from source code to object code. While source code is written in a high-level, human-readable programming language (e.g., C, Java), object code is in a binary format that is not immediately comprehensible. Although object code can be transformed into human-readable form in a process known as disassembly, a human operator cannot efficiently and reliability detect malicious computer programs by inspecting the disassembled object code.

However, in some aspects, attributes of computer programs or files may be derived from object code or may be predefined. An attribute can refer to any salient data point that can be measured from a sample. Attributes characterizing a file and/or the environment in which the file is being, read, downloaded, executed, written to, etc. can be collected or otherwise accessed. For example, binary files may be reviewed and/or classified based on one or more attributes associated with the file. Determined attributes can include binary attributes, integer attributes, and/or the like. For example, attributes can include information/indications relating to a file size, a number of file headers, a portable execution (PE) checksum, debugging, an operating system or image version, a number of sections, entropy of code sections, a number of resources, an entry point, registry keys, registry startup keys, networking related dynamic link libraries (DLL), high frequency DLLs in benign files, file size, and/or any characteristic of a file which may be relevant to classification of a file.

Once attributes of a file are determined, the file may be mapped in a coordinate space defined by at least some of the attributes. Models/mapping can be used to define the likelihood of any sample to be classified in a particular class. For example, based upon where the files are mapped in the attribute coordinate space, the files may be classified as malicious, benign, clean, safe, etc. As used herein, the terms “safe” and “unsafe” can generally refer to a machine-generated, relative classification based on an estimate of the danger presented by one or more files that could be a threat. In some embodiments a threat score can be generated for a given file, which can be used to classify the file as safe or unsafe. Other classification protocols with more than two divisions are also within the scope of the current subject matter. In non-limiting examples, threat score outputs from one or more machine learning threat discernment models can be used to classify potential threats in several categories (e.g. safe, suspect, benign, unsafe, malicious, downloader, Trojan, adware, and/or the like).

However, general determinations based on locations in a mapping may not always be accurate. Therefore, machine learning may be utilized to increase the accuracy of these determinations, as described herein. For example, in some aspects, a convolutional neural network (CNN) can be utilized to analyze the disassembled binary file including by applying a plurality of kernels adapted to detect certain sequences of instructions in the disassembled binary file. In some implementations, a convolutional neural network can be trained to detect malware by applying one or more supervised learning and/or optimization techniques. For instance, training a convolutional neural network can include utilizing the convolutional neural network to process a plurality of training files. The training files can be disassembled binary files with known classifications (e.g., as malicious or benign), and the convolutional neural network can be utilized to process training files and generate corresponding training outputs.

For example, samples can be analyzed to provide predictive outputs regarding the likelihood of a given file being a threat. Samples can refer to any piece of data upon which classifying or similarity analysis operations against similar samples can be performed.

Training the convolutional neural network can include applying one or more supervised learning and/or optimization techniques to minimize the error associated with the training outputs (e.g., relative to the known classifications). In some embodiments, a trained convolutional neural network can be provided to detect malicious executables by determining classifications of unknown disassembled binary files.

In some aspects, based upon a classification (e.g., based upon a location in a mapping) a decision can be made as to whether or not to allow the file to execute, be downloaded, opened, etc. If it is determined that the file should not execute or some other action with the file should be prevented, a process or module can take action to prevent the file from executing, opening, continuing to execute, writing, being downloaded, and/or the like. In some implementations, the file can be quarantined if it is assessed as a potential threat.

By carefully selecting and training the machine learning threat discernment models in an ensemble of such models, a threat discernment system can act resiliently against change over time, accommodating small and large changes in program behaviors that resemble “safety” or a lack thereof. Machine learning threat discernment models may be characterized by one or more algorithms incorporated therein, which may include, as illustrative examples, neural networks, support vector machines, logistic regression models, Bayesian algorithms, and decision trees. Consistent with the current subject matter, machine learning models can implemented as both cloud-based instances and locally running instances (e.g. at one or more endpoint computers of an organization).

FIG. 1 illustrates an example of a system 100 in which file classification and information retrieval solutions may be implemented, in accordance with some embodiments. As illustrated, an endpoint 120 may communicate with other endpoints 120, a computing system 110, and/or an external service provider 140 through the use of a network 150. The endpoint 120 may include one or more of a phone, mobile device, tablet, personal computer, or other device. In accordance with various embodiments, the endpoint 120 may operate according to an iOS, Android, Mac, Windows, Linux, or other operating system. The network 150 can include one or more of a local area network (LAN), a wireless LAN (WLAN), a personal area network (PAN), a wide area network (WAN), a cellular network, the internet, and/or the like. In some aspects, an endpoint 120 may communicate directly with other devices through Bluetooth (e.g., low energy) communications, Near-field communications (NFC), ZigBee communications, a universal serial bus (USB), wireless USB, device-to-device communications, and/or the like.

As further illustrated, an endpoint can include a memory 122 and a file classification system 124. The memory 122 can be configured to hold a plurality of files, in addition to executable software for controlling the operation of the endpoint 120. The file classification system 124 may include software and/or hardware components, and may be configured to operate in accordance with the procedures described herein. For example, in various embodiments, the file classification system 124 may be configured to classify a plurality of files as malicious (or benign) or clean. Although a generally binary classification scheme is described, the file classification system 124 may be configured to classify files in more than one manner and/or the classification may include more than two options (e.g., a sliding scale of how clean or malicious a file may be).

As further illustrated, the computing system 110 can include a memory 112, a file classification system 114, a centroid generation system 116, and/or an update system 118. Similar to above, the memory 112 may be configured to store a plurality of files and/or software for controlling operation of the computing system 110. In some aspects, the computing system 110 may be in communication with a database 130, and can additionally or alternatively use the database 130 for file storage. Although illustrated separately, the computing system 110 can operate as a server which includes the database 130.

The file classification system 114 may be similar to the file classification system 124 on the endpoint, in that the file classification system 114 may be configured to process and/or classify files (e.g., as malware, benign, or clean). However, the file classification system 114 may include additional functionalities which are not present on the file classification system 124 of the endpoint 120, such as an ability to process files from multiple endpoints 120 and/or utilize machine learning to more appropriately classify files. In some embodiments, the file classification system 124 on the endpoint 120 may not be configured to process as much information as the file classification system 114 of the computing system 110, which can save valuable processing resources for other processes running on the endpoint 120.

In various embodiments, either or both of the file classification systems 114, 124 can be configured to utilize centroids to identify and/or classify files. As referred to herein, a centroid can be a space (e.g., geometric or otherwise) in which files are classified as malicious, benign, clean, and/or the like. Although centroids are generally described as being used for the classification of malicious files, centroids can be used for other file classification schemes. Furthermore, centroids can be used as part of an ensemble of multiple different types of models (some or all of which may be centroid-based models) that in parallel and/or in serial fashion analyze a particular file or portion thereof. In some cases, there can be cascading models in which the centroids are used to route files/samples to other specific models (as opposed to a binary determination of some sort). Additional details on the contents and use of centroids are provided below. The centroid generation system 116 may be used to create centroids for use by either or both of the file classification systems 114, 124, as described herein. In order to provide updates to the file classification system 124 at the endpoint 120, the computing system 110 may utilize an update system 118. For example, the update system 118 may be configured to provide patches containing information on a current set of centroids to the endpoint 120. Thus, the endpoint 120 may be able to stay up to date and increase the likelihood of preventing an attack (e.g., prevent the endpoint 120 from opening a worm, Trojan horse, spyware, and/or the like as the computing system 110 accounts for their possible existence).

In some implementations, the computing system 110 can communicate with an external service provider 140. For example, the computing system 110 can communicate directly with the external service provider 140 and/or may communicate over the network 150 with the external service provider 140. The external service provider 140 can be configured to receive information and/or provide information in order to provide a software/application/processing service to the computing system 110 and/or an endpoint 120. For example, in some aspects, the external service provider 140 can provide additional functionalities which the computing system 110 may not be enabled to perform.

In some cases, there can be intermediate operations prior to the file classification system 114 and/or the file classification system 124 processing and/or classifying files (e.g., as malware, benign, or clean). For example, the output of the centroid generation system 116 can comprise routing centroids which can cause a particular sample/file to be routed (e.g., by uploading from the endpoint 120 to the computing system 110) to a secondary mechanism (e.g., a different type of computer-implemented model) for further analysis.

As one example, the endpoint 120 scans a file and determines that it is a sample compiled with .NET code. It is determined that the .NET sample falls in a .NET centroid which causes the endpoint 120 to cause the .NET sample to be uploaded to a secondary model on the computing system 110 which is designed to expertly analyze only .NET samples. The secondary model executed on the computing system 110 then returns a classification to the end point.

FIG. 2 illustrates an example mapping 200 of a plurality of files 205 in space, in accordance with some embodiments. In some aspects, the mapping 200 can be referred to as a “feature space.” As illustrated, the mapping 200 can be a two-coordinate system based on an intersection of attribute_(A) 210 and attribute_(B) 220. However, one or both of the attributes 210, 220 may be a complex attribute and/or the mapping 200 can be an N-coordinate system, where N represents the number of attributes used for file classification. In some embodiments, N may include all known attributes of a file 205. If more attributes are used to classify the files 205, it may be more likely that malicious (or benign) files 205 are grouped together and/or that clean files 205 are grouped together. However, some attributes may not be as relevant or may be counterproductive to a file classification scheme, and may not be considered in the mapping 200. Although the mapping 200 is illustrated as only containing an intersection of positive values for attribute_(A) 210 and positive values for attribute_(B) 220, negative values can be additionally or alternatively used, where applicable.

As illustrated, the mapping 200 can include a decision boundary 230 which divides the mapping 200 into a lower portion 240 and an upper portion 250. In the illustrated embodiment, the lower portion is generally considered to contain files 205 which are clean, and the upper portion 250 is generally considered to contain files 205 which are malicious. However, as illustrated, there may be malicious files 205 in the lower portion 240 which were not properly classified by the decision boundary 230. Similarly, there may be clean files 205 in the upper portion 250 which were not properly classified by the decision boundary 230. Thus, methods for identifying these improper classifications and/or properly identifying malicious and/or clean files 205 are provided. For example, the centroid 245 in the lower portion 240 may be utilized to identify a region of the lower portion 240 in which malicious files are located. Similarly, the centroid 255 in the upper portion 250 may be utilized to identify a region of the upper portion 250 in which clean files are located. Thus, some combination of the decision boundary and the centroids 245, 255 can be used to properly classify the files 205 (e.g., as malicious, benign, clean, safe, etc.). Although the decision boundary 230 is illustrated as a linear function, other functions are possible, such as an exponential function, a sine function, a logarithmic function, some combination thereof, and/or the like. In some aspects, a coordinate space can be divided into more than two regions. For example, in some embodiments, a space can be divided into a non-malicious region, a malicious region, and an adware region. Any number of regions can be used with the classifications described herein (e.g. safe, suspect, benign, unsafe, malicious, non-malicious, downloader, Trojan, adware), and/or the like. In various embodiments, more than one decision boundary 230 can be used to accomplish multiple divisions. Further, although the lower portion 240 is described as classifying malicious files and the upper portion 250 is described as classifying clean files, the opposite may be true and/or the classification based on the location of files 205 relative to the decision boundary 230 can vary.

As above, the centroids 245, 255 may be defined by and/or represented as geometric shapes. For example, the centroid 245 can be defined by at least a center point and radius such that the centroid 245 includes a circle. In this example, the region within the circle of the centroid 245 can be considered as a region in which malicious files are located. This region can be excluded from a search for clean files and/or included in a search for malicious files. As such, centroids can be used to indicate portions of the mapping 200 where the files 205 are misclassified, portions of the mapping 200 where malicious files 205 are located, and/or portions of the mapping 200 where clean files 205 are located. Various systems and methods for creating centroids are described herein. Although the centroids 245, 255 are illustrated as circles, other geometric shapes are possible, such as triangles, rectangles, pentagons, polygons, and/or other geometric shapes, which can extend to more than two dimensions.

In some aspects, the centers of the illustrated files 205 can represent the actual location of the files 205. However, these illustrations are merely demonstrative, and the relative locations of the files 205 may not be the same in actual embodiments. For example, while a file 205 is located near the edge of the centroid 245, in some embodiments, the location of this file 205 may be exactly on the boundary line of the centroid 245. However, as described herein, an offset can be used such that the file 205 is not located exactly on the boundary.

In some aspects, when a system model is updated (e.g., attributes for the files 205 are added, removed, altered, etc.), the locations of at least a portion of the files 205 in the mapping 200 may shift. For example, the shifted locations of the files 205 are illustrated in FIG. 3.

FIG. 3 illustrates an example mapping 300 of a plurality of files 305 in space, in accordance with some embodiments. The illustrated files 305 can be the same as (or similar to) the files 205 of FIG. 2, but may be classified according to a different set of attributes, which can cause at least some of the files 305 to be mapped to different locations. Similar to FIG. 2, the mapping 300 of the files 305 is based on an intersection of a first attribute_(A) 310 and a second attribute_(B) 320, and includes an upper portion 340 and a lower portion 350 separated by a decision boundary 330. The attributes 310, 320 may be the same as or similar to the attributes 210, 220 of FIG. 2, or may account for a different set of file attributes.

As illustrated, not all of the malicious files 305 may be present in the region of the centroid 345 compared to the prior version of the files 205 in the region of the centroid 245 of FIG. 2. Similarly, not all of the clean files 305 may be present in the region of the centroid 355 compared to the prior version of the files 205 in the region of the centroid 255 of FIG. 2, and/or malicious files 305 may now be present within the region of the centroid 355. Thus, one or more of the centroids 345, 355 may need to be redefined after a system update/rebuild.

FIG. 4 illustrates an example of a mapping 400 including a centroid 445, in accordance with some embodiments. As illustrated, the centroid 445 may be located in the lower portion 440 of a mapping 400 (defined at least in part by the decision boundary 430).

In some embodiments, the mapping 400 can be formed by mapping files according to two or more attributes associated with the files. The files that are mapped can include test/training files which are known to be malicious and/or files which are known to be non-malicious. Once the mapping 400 is formed, a decision boundary 430 can be formed to separately classify malicious (and/or benign) and non-malicious (and/or non-benign) files. In some embodiments, the decision boundary 430 may not be used, and a region inside or outside a plurality of centroids can be searched.

After the mapping 400 is formed, clusters of files can be identified based on one or more clustering algorithms. For example, one or more of a density-based spatial clustering of applications with noise (DBSCAN) algorithm, a k-mean clustering, hierarchical agglomerative clustering, and/or the like may be used to identify and/or group clusters of files in the mapping. In the illustrated mapping 400, files X₁₋₅ may be identified as being part of a cluster of malicious files in the lower portion 440 that is used to classify files as non-malicious. Thereafter, the centroid 445 may be generated such that it includes all of the files X₁₋₅. Specifically, the center point 447 and the radius 449 of the centroid 445 can be generated such that all of the files X₁₋₅ are included within the region of the circle formed by the center point 447 and the radius 449.

In various embodiments, centroids can be full-space centroids, full-projected centroids, or subspace centroids. In some embodiments, the files in the mapping 400 can be classified based on N separate attributes. N can include all attributes of the mapped files or some portion thereof. Full-space centroids can be generated based on averaging each of the N attributes for the files in an identified cluster. Subspace centroids, on the other hand, can be generated based on only a portion of the N attributes which are shared by the files in an identified cluster. For example, Table 1 includes an example of attribute values for the plurality of malicious files X₁₋₅ identified as a cluster.

TABLE 1 Attribute File 1 2 3 4 5 6 7 X₁ 0 1 1 1 1 1 1 X₂ 0 1 1 1 2 1 2 X₃ 0 1 1 1 2 2 1 X₄ 0 1 0 1 4 0.5 0 X₅ 1 1 0 1 5 0.5 0 Average 0.2 1 0.6 1 2.8 1 0.8

As shown by Table 1, each of the files X₁₋₅ can include seven attributes (e.g., N=7). These attributes can be represented by binary values, integer values, fractional values, and/or the like. In order to generate a full-space centroid, the average values for each of the attributes can be calculated, and these values can be used to define the center point 447 (e.g., within a seven-dimensional Euclidian space). Additionally, the Euclidian distance between each of the files X₁₋₅ can be calculated. For example, for any two file locations p and q, the Euclidian distance can be determined based on the following formula:

$\begin{matrix} {{d\left( {p,q} \right)} = {d\left( {q,p} \right)}} \\ {= \sqrt{\left( {q_{1} - p_{1}} \right)^{2} + \left( {q_{2} - p_{2}} \right)^{2} + \ldots + \left( {q_{n} - p_{n}} \right)^{2}}} \\ {= \sqrt{\sum\limits_{i = 1}^{n}\;\left( {q_{i} - p_{i}} \right)^{2}}} \end{matrix}.$

In various embodiments, the radius 449 can be set to equal the maximum Euclidian distance between any two of the files X₁₋₅. In other embodiments, the radius 449 can be set to equal the maximum distance (e.g., Euclidian) between the center point 447 and any one of the files X₁₋₅. In accordance with various embodiments, an offset can be added to the radius 449 such that the centroid is not overly exclusive and/or accounts for computing round-offs. For example, a floating point delta can be set to a small number (e.g., 0.00000001) and added to whatever distance is calculated (e.g., the maximum distance or any other distance). Other methods for determining the radius 449 and/or the distance between points may be used, and/or statistical properties of a group of distances other than a maximum may be used (e.g., mean, median, mode, etc.).

In order to generate a subspace centroid, the values for each of the attributes which are shared among all of the files can be calculated, and these values can be used to define the center point 447. For example, in Table 1, Attribute₂ and Attribute₄ have a “1” common to all of the files X₁₋₅. Therefore, a center point 447 of a subspace centroid for the files X₁₋₅ can be defined with a value of “1” in Attribute₂ and Attribute₄, and a value of “0” in the remaining attributes. Similar to above, the distance between the files X₁₋₅ can be calculated, and the maximum distance can be set as the radius 449. However, in other embodiments, the radius can be set to equal “1”, and only Attribute₂ and Attribute₄ are searched to determine whether an unknown file can be considered as part of the subspace centroid. In related embodiments, an unknown file might only be regarded as part of the subspace centroid if the distance between the center point 447 and the file is exactly “1” for the searched attributes (e.g., Attribute₂ and Attribute₄). The remaining attributes may be ignored for this purpose. In some aspects, the center point 447 of a subspace centroid might only be based on attributes where all clustered files (e.g., files X₁₋₅) equal “1”. However, other values are possible.

Full-projected centroids can be similar to full-space centroids, but may be based on a scan of only a subset of the file attributes. For example, when “scanning” (e.g., reviewing attributes of files with known classifications) for clusters to generate centroids, only some of the attributes of the files will be used to determine how close files are to each other (e.g., to identify clusters). This subset of attributes can be manually selected and/or may be selected/controlled through machine learning. In some aspects, once clusters are identified, the centroids defined to encompass the files may be based on all of the attributes of the files. Not doing so could possibly exclude a file (e.g., the radius of the centroid may be too small and a file may lie beyond this distance). In some aspects, random projection and/or related techniques can be used to map attributes in a higher dimensional space to a lower dimensional space.

Although Euclidian distance is used for several measurements described herein, other distance measurement techniques may additionally or alternatively be used. For example, an L1-norm distance, a cosine distance, and/or the like. Although the upper portion 450 is not discussed, it may be scanned in a manner similar to the lower portion 440, and/or centroids may be defined within the upper portion 450 based on the scan. Although the centroids are not illustrated as crossing over the decision boundaries, in some embodiments, the centroids may be defined in such a manner that they do. For example, in some embodiments, the region of the centroid 445 may cover portions of the mapping 400 on both sides of the decision boundary. In this case, the centroid 445 could be defined to include only malicious files or only clean files, and may be utilized in a search accordingly.

Although the centroid 445 includes multiple files X₁₋₅ which were misclassified as safe files, the centroid 445 also includes multiple files O₁₋₃ which are actually safe files. This may be identified by searching the centroid 445 to identify whether it includes any known safe files. In any event, the parameters of the centroid 445 may be modified based on determining that it is over-inclusive, at a risk of being over-inclusive, or based on one or more “hyper parameters.” For example, in some aspects, a maximum value of a radius can be set, and any centroid which is generated that includes a radius that is greater than the maximum value can be reduced until it is equal to or less than the maximum value. In some aspects, this can require two or more centroids to cover the regions which were previously covered by one centroid, but this may also decrease the likelihood of misclassification. However, increasing the number of centroids can increase the amount of computing resources necessary to conduct a file search. Therefore, machine learning can be utilized (e.g., via an iterative process) to determine values for one or more of the hyper parameters that provide an optimized balance of these concerns.

FIG. 5 illustrates another example of the mapping 400 of FIG. 4 including multiple centroids 545, 555, in accordance with some embodiments. As before, the malicious files X₁₋₅ are misclassified as clean files, but are actually malicious files. However, the previous centroid 445 was over-inclusive, and reducing the value of the radius 449 was insufficient to remove the safe files O₁₋₃ while still keeping the malicious files X₁₋₅ within one centroid. Therefore, two centroids 545, 555 may instead be used. In some embodiments, a procedure may be used to determine that files X₁₋₃ form a cluster (or a sub-cluster) and/or that files X₄₋₅ form another cluster (or sub-cluster). This can be determined based on one or more of the clustering techniques described herein, and/or may be determined based on using distances (e.g., Euclidian) to identify files which are in close proximity to each other (e.g., as compared to other files).

Whichever technique is used to identify additional clusters of files, once the additional clusters are identified a centroid can be generated to encompass the files. For example, as illustrated, centroid 545 can be defined by the center point 547 and the radius 549 such that it includes files X₁₋₃. Similarly, centroid 555 can be defined by the center point 557 and the radius 559 such that it includes files X₄₋₅. Based on these centroids 545, 555, a search of the mapping 400, for example, may be conducted such that malicious files are excluded.

In some embodiments, once centroids are satisfactorily generated, they may be stored. In order to determine whether the centroids are satisfactory, iterative procedures can be performed. For example, in scanning for clusters, certain parameters may be used such that the identified clusters are more valuable for centroid generation. The parameters can include a maximum and/or minimum number of files per cluster, a maximum and/or minimum distance between files in a cluster, a maximum and/or minimum number of clusters, a maximum and/or a minimum ratio of the number of clusters to the number of files, and/or the like. These parameters can be hyper parameters which apply to all scans for clusters (e.g., a maximum Euclidian distance between any one file in the cluster to any other file), or may be parameters which can be changed from one scan to the next (e.g., a maximum number of files in a cluster).

In some aspects, centroids may not be generated until after identified clusters include certain characteristics. For example, if the files X₁₋₅ in FIG. 4 are identified as being too far apart from each other and/or the safe files O₁₋₃ are identified as being too close to the cluster (e.g., part of the cluster), then the centroid 445 may never be generated. Instead, another scan for clusters could be performed such that the new clusters which properly exclude the clean files O₁₋₃ are identified. Although the stored version of the centroids may not cover all of the re-classifications (e.g., re-classifying malicious files present in a region marked as safe) necessary to correctly classify every single file, the classification system utilizing centroids is more accurate than other classification systems which do not use centroids. Centroid 545 and/or centroid 555 can be defined based on one or more of the techniques described herein, and need not be formed through the same technique.

Whatever methods used to generate centroids, once the centroids are defined, a new file (e.g., a file within an unknown classification) can be compared against a decision boundary and/or the centroids to determine a classification for the file. In some embodiments, comparing a new file can include mapping the new file to a location in the coordinate space based on a plurality of attributes for the new file, comparing the location of the new file against the center points and radiuses of the centroids, and/or comparing the location of the new file against a decision boundary. If the distance between the new file location and the center point of any given centroid is less than or equal to the radius for the centroid, then the new file may be considered as being within the centroid (and may be classified according to a classification of the centroid). If the new file is determined to be located outside of all defined centroids, then the location of the new file can be compared against at least one decision boundary to determine which portion of the coordinate space the new file lies within (e.g., within a portion classified as malicious). Based upon this comparison, one or more classification types may be associated with the new file and/or stored. In some aspects, this comparison can be referred to as “searching” for the file or a classification thereof.

In some aspects, centroids can be generated and/or used without the use of a decision boundary 430. For example, FIG. 6 illustrates an example of a mapping 600 including a plurality of centroids 645, 655, 665, in accordance with some embodiments. In some embodiments, a plurality of training files can be mapped into a coordinate space, and the centroids 645, 655, 665 can be generated based on clustering, regardless of where the files/clusters are located in the coordinate space. In accordance with these embodiments, the training files may be classified according to one or more classifications before (and/or after) the centroids are generated. For example, if the files X₁₋₅ are classified as “malware,” the centroid 645 can be generated to include files X₁₋₃ and the centroid 655 can be generated to include files X₄₋₅. Thus, files which are determined to lie within either of the centroids 645, 655 can be classified a malware. Files O₁₋₂ can be classified as “clean,” and the centroid 665 can be generated to include files O₁₋₂.

Once the centroids 645, 655, 665 are generated, files within the centroids 645, 655 (and/or the centroids 645, 655 themselves) can be (re)classified. For example, files X₁₋₃ (and/or the centroid 645) can be (re)classified as malware type A and/or files X₄₋₅ (and/or the centroid 655) can be (re)classified as malware type B. Thereafter, when new files Y, Z (e.g., files which are not known to be malicious or non-malicious) are compared against the centroids 645, 655, 665, the comparison may classify the new files Y, Z as malware type A if the new file Y is determined to lie within the centroid 645, or may classify the new file as malware type B is the file Z is determined to lie within the centroid 655. Doing so can provide additional insight on the classification of a file, and/or does not require the use of a decision boundary, which can reduce processing time and/or resources necessary to classify files (e.g., prevent the execution of malicious files). In some embodiments, all centroids that exist within the system may be searched, only a portion of the centroids might be searched, and/or the centroids can be searched until a match is found for a given file. In some embodiments, clusters might only be selected and/or centroids might only be generated when a grouping of files is limited to one type (or subtype). As described herein, other classification types and/or subtypes may additionally or alternatively be used.

FIG. 7 illustrates an example of a computing apparatus 700, in accordance with various embodiments. An apparatus consistent with at least a portion of the computing apparatus 700 may be used to implement one or more of the endpoints 120, the computing system 110, the database 130, and/or the external service provider 140. In some aspects, the computing apparatus 700 may be used to perform at least some of the processes described herein. Additionally or alternatively, a plurality of computing apparatus 700 can be used to perform at least some of the processes described herein.

As illustrated, the computing apparatus 700 may include one or more processors such as processor 710 to execute instructions that may implement operations consistent with those described herein. The computing apparatus 700 may include memory 720 to store executable instructions and/or information. Memory 720 may include solid-state memory, solid-state disk drives, magnetic disk drives, or any other information storage device. Memory 720 may provide storage for at least a portion of a database. The computing apparatus 700 may include one or more displays, such as display 730. As illustrated, the computing apparatus 700 may also include a network interface 740 to a wired network or a wireless network, such as the network 150 of FIG. 1. Wireless networks may include WiFi, WiMax, and cellular networks (2G/3G/4G/5G), and/or any other wireless network. The computing apparatus 700 may include one or more user interfaces, such as user interface 750. The user interface 750 can include hardware or software interfaces, such as a keyboard, mouse, or other interface that may include a touchscreen integrated with a display 730.

FIG. 8 illustrates an example of a method 800 for searching for files, in accordance with various embodiments. Although method 800 is illustrated as having a specific operational flow, two or more steps may be combined into a single step, a single step may be performed in one or more steps, one or more of the illustrated steps may not be present in various implementations, and/or additional steps not illustrated may be part of the method 800. In various embodiments, the method 800 may be performed by one or more of an endpoint 120, the computing system 110, the database 130, the external service provider 140, and/or the computing apparatus 700. In some aspects, the computing apparatus 400 may be regarded as a server.

Method 800 may start at operational block 810 where the apparatus 700, for example, maps a plurality of files in a coordinate space defined by one or more attributes of the plurality of files. In some aspects, the coordinate space may be similar to the coordinate space of FIG. 2, defined by Attribute_(A) 210 and Attribute_(B) 220, and the plurality of files may be similar to the files 205. In some aspects, the coordinate space can be defined based on an intersection of at least two attributes, or some function of the intersection of the at least two attributes.

Method 800 may next proceed to operational block 820 where the apparatus 700, for example, divides the coordinate space into at least a first portion and a second portion based on a function. In some aspects, the function may be similar to the decision boundary 230 of FIG. 2.

Method 800 may next proceed to operational block 830 where the apparatus 700, for example, classifies files in the first portion as malicious and/or files in the second portion as not malicious. In some aspects, a first subset of the plurality of files in the first portion can be classified as malicious, and/or a second subset of the plurality of files in the second portion can be classified as not malicious. Although malicious and not malicious are described, other classifications may be used.

Method 800 may next proceed to operational block 840 where the apparatus 700, for example, defines one or more first geometric regions in the first portion that (re)classifies files as not malicious. In some embodiments, the one or more first geometric regions can be defined such that it includes all of the non-malicious files from the first portion (e.g., the misclassified files). In some aspects, the one or more first geometric regions can be similar to the centroid 255 in the upper portion 250 of FIG. 2.

Method 800 may next proceed to operational block 850 where the apparatus 700, for example, defines one or more second geometric regions in the second portion that (re)classifies files as malicious. In some embodiments, the one or more second geometric regions can be defined such that it includes all of the malicious files from the second portion (e.g., the misclassified files). In some aspects, the one or more first geometric regions can be similar to the centroid 245 in the lower portion 240 of FIG. 2.

Method 800 may next proceed to operational block 860 where the apparatus 700, for example, searches for a non-malicious file by searching in the one or more first geometric regions and/or within a region of the second portion not including the one or more second geometric regions. Although the searching mechanisms described generally relate to malicious and non-malicious files, other file classifications may be utilized such that a classification and search may be for any type of file. Similarly, although the file classification systems described generally use a binary classification (e.g., malicious or not malicious), classifications systems utilizing more than two classifications are possible, in accordance with some embodiments.

Method 800 may next proceed to operational block 870 where the apparatus 700, for example, prevents execution of a file located in the one or more second geometric regions or within a region of the first portion not including the one or more first geometric regions. Determining whether a file is located within a geometric region can include determining whether a distance between a center point of the geometric regions and a location of the file is less than or equal to a radius of the geometric region. If this is true, than the file can be regarded as being included within the geometric region.

Additionally or alternatively, method 800 can include the apparatus 700, for example, identifying a plurality of clusters from the plurality of files, determining whether any of the plurality of clusters do not include known malicious files, and/or defining individual geometric regions around each of the plurality of clusters which do not include known malicious files, wherein the one or more first geometric regions includes the individual geometric regions. Similarly, the apparatus 700 can determine whether any of the plurality of clusters include known malicious or benign files, and/or defining individual geometric regions around each of the plurality of clusters which do include known malicious or benign files. In some embodiments, method 800 can include the apparatus 700, for example, determining whether any of the individual geometric regions include a radius greater than a threshold value, reducing the radius of the individual geometric regions which are greater than the threshold value such that the radius is less than or equal to the threshold value, and/or re-defining, after the reducing, the individual geometric regions which no longer include all files from a respective cluster of the plurality of clusters, wherein the re-defining includes defining multiple smaller geometric regions in place of the individual geometric regions.

In some aspects the one or more first geometric regions can include a circular (and/or spherical) geometry having a center point and a radius. Additionally or alternatively, the center point can be determined based at least in part on averaging locations for each of the plurality of files located within the one or more first geometric regions. Similarly, the center point can be determined based at least in part on averaging shared attributes for each of the plurality of files located within the one or more first geometric regions. In related aspects, the radius can be determined based at least in part on a maximum Euclidian distance between each of the plurality of files located within the one or more first geometric regions.

FIG. 9 illustrates another example of a method 900 for searching for files, in accordance with various embodiments. Although method 900 is illustrated as having a specific operational flow, two or more steps may be combined into a single step, a single step may be performed in one or more steps, one or more of the illustrated steps may not be present in various implementations, and/or additional steps not illustrated may be part of the method 900. In various embodiments, the method 900 may be performed by one or more of an endpoint 120, the computing system 110, the database 130, the external service provider 140, and/or the computing apparatus 700. In some aspects, the computing apparatus 400 may be regarded as a server.

Method 900 may start at operational block 910 where the apparatus 700, for example, determines a plurality of attributes of a plurality of files. These attributes can include any number of salient data points that can be measured from a file, characterizing a file and/or the environment in which the file is being, read, downloaded, executed, written to, and/or the like.

Method 900 may next proceed to operational block 920 where the apparatus 700, for example, maps the plurality of files in a coordinate space defined by an intersection of at least two of the plurality of attributes. The coordinate space can be N-dimensional, where N corresponds to the number of attributes used to process the plurality of files.

Method 900 may next proceed to operational block 930 where the apparatus 700, for example, searches for one or more clusters among the plurality of files in the coordinate space. The clusters can be identified through a database scan or other method, as described herein.

Method 900 may next proceed to operational block 940 where the apparatus 700, for example, defines one or more geometric regions, around the one or more clusters, classifying a set of files within the one or more geometric regions as belonging to a specific classification type. The one or more first geometric regions can be centroids, which are created and/or used as described herein. In some aspects, the one or more first geometric regions can include a circular geometry, and/or can have a center point and a radius. The center point can be determined based on averaging locations for each of the plurality of files located within the one or more first geometric regions. In some embodiments, the center point can be determined based on shared attributes for each of the plurality of files located within the one or more first geometric regions. The radius can be determined based on a maximum Euclidian distance between each of the plurality of files located within the one or more first geometric regions.

Method 900 may next proceed to operational block 950 where the apparatus 700, for example, determines whether the file is located within the one or more first geometric regions based on whether a distance between a center point of the one or more first geometric regions and a location of the file is less than or equal to the radius. Distance can be calculated through Euclidian distance between a location of the file and a location of the center points, or other methods of determining distances between two points in an N-dimensional space.

Method 900 may next proceed to operational block 960 where the apparatus 700, for example, classifies a file as belonging to the specific classification type when the file is located within the one or more first geometric regions. In some aspects, the specific classification type can include one or more of safe, suspect, benign, unsafe, malicious, non-malicious, downloader, Trojan, adware, and/or the like. If the file is determined to be malicious, it may be prevented from being executed and/or a warning can be provided regarding the file. Malicious can refer to viruses, worms, Trojan horses, spyware, and/or programming that gathers information about a computer/user without permission.

One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs, field programmable gate arrays (FPGAs) computer hardware, firmware, software, and/or combinations thereof. These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

These computer programs, which can also be referred to programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural language, an object-oriented programming language, a functional programming language, a logical programming language, and/or in assembly/machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and/or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example as would a processor cache or other random access memory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, such as for example visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including, but not limited to, acoustic, speech, or tactile input. Other possible input devices include, but are not limited to, touch screens or other touch-sensitive devices such as single or multi-point resistive or capacitive trackpads, voice recognition hardware and software, optical scanners, optical pointers, digital MRI image capture devices and associated interpretation software, and the like. As used herein, the term “module” refers to software functionality that can be implemented via one or more programmable processors that are part of one or more computing systems.

In the descriptions above and in the claims, phrases such as “at least one of” or “one or more of” may occur followed by a conjunctive list of elements or features. The term “and/or” may also occur in a list of two or more elements or features. Unless otherwise implicitly or explicitly contradicted by the context in which it used, such a phrase is intended to mean any of the listed elements or features individually or any of the recited elements or features in combination with any of the other recited elements or features. For example, the phrases “at least one of A and B;” “one or more of A and B;” and “A and/or B” are each intended to mean “A alone, B alone, or A and B together.” A similar interpretation is also intended for lists including three or more items. For example, the phrases “at least one of A, B, and C;” “one or more of A, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, B alone, C alone, A and B together, A and C together, B and C together, or A and B and C together.” Use of the term “based on,” above and in the claims is intended to also mean, “based in part on” and/or “based at least in part on,” such that an unrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems, apparatus, methods, and/or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and subcombinations of the disclosed features and/or combinations and subcombinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and/or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations can be within the scope of the following claims. 

The invention claimed is:
 1. A system, comprising: at least one processor; and at least one memory including program code which when executed by the at least one memory provides operations comprising: classifying at least a portion of a plurality of files as malicious based on a function dividing a coordinate space into at least a first portion and a second portion, wherein the first portion includes a first subset of the plurality of files classified as malicious; defining one or more first geometric regions in the first portion that classify files from the first subset as not malicious; identifying a plurality of clusters from the plurality of files; determining whether any of the plurality of clusters do not include known malicious files; defining individual geometric regions around at least one of the plurality of clusters which do not include known malicious files, wherein the one or more first geometric regions include the individual geometric regions; determining whether a file is malicious based on whether the file is located within the one or more first geometric regions; and preventing files determined to be malicious from such files from executing, opening, continuing to execute, writing, or being downloaded.
 2. A system as in claim 1, wherein the second portion includes a second subset of the plurality of files classified as not malicious, and wherein the operations further comprise: defining one or more second geometric regions in the second portion that classify files from the second subset as malicious, wherein determining whether the file is malicious further comprises determining whether the file is located within a region of the second portion that does not include the one or more second geometric regions.
 3. A system as in claim 1, wherein the operations further comprise: determining a plurality of attributes of the plurality of files; and mapping the plurality of files in a positive portion of the coordinate space defined by an intersection of at least two of the plurality of attributes.
 4. A system as in claim 1, wherein the operations further comprise: determining whether any of the individual geometric regions include a radius greater than a threshold value; reducing the radius of the individual geometric regions which are greater than the threshold value such that the radius is less than or equal to the threshold value; and re-defining, after the reducing, the individual geometric regions which no longer include all files from a respective cluster of the plurality of clusters, wherein the re-defining includes defining multiple smaller geometric regions in place of the individual geometric regions.
 5. A system as in claim 1, wherein the one or more first geometric regions include a circular geometry having a center point and a radius, and wherein the file is determined to be located within the one or more first geometric regions when a distance between the center point and a location of the file is less than or equal to the radius.
 6. A system as in claim 5, wherein the center point is determined based on averaging locations for each of the plurality of files located within the one or more first geometric regions.
 7. A system as in claim 5, wherein the center point is determined based on shared attributes for each of the plurality of files located within the one or more first geometric regions.
 8. A system as in claim 5, wherein the radius is determined based on a maximum Euclidian distance between each of the plurality of files located within the one or more first geometric regions.
 9. A system as in claim 1, wherein the classifying employs at least one machine learning model.
 10. A system as in claim 1, wherein the classifying employs at least one of: a neural networks, a support vector machine, a logistic regression model, a Bayesian algorithm, or a decision tree.
 11. A computer-implemented method, comprising: classifying at least a portion of a plurality of files as malicious based on a function dividing a coordinate space into at least a first portion and a second portion, wherein the first portion includes a first subset of the plurality of files classified as malicious; defining one or more first geometric regions in the first portion that classify files from the first subset as not malicious; identifying a plurality of clusters from the plurality of files; determining whether any of the plurality of clusters do not include known malicious files; defining individual geometric regions around at least one of the plurality of clusters which do not include known malicious files, wherein the one or more first geometric regions include the individual geometric regions; determining whether a file is malicious based on whether the file is located within the one or more first geometric regions; and preventing files determined to be malicious from such files from executing, opening, continuing to execute, writing, or being downloaded.
 12. A computer-implemented method as in claim 11, wherein the second portion includes a second subset of the plurality of files classified as not malicious, wherein the method further comprises: defining one or more second geometric regions in the second portion that classify files from the second subset as malicious, and wherein determining whether the file is malicious further comprises determining whether the file is located within a region of the second portion that does not include the one or more second geometric regions.
 13. A computer-implemented method as in claim 11, further comprising: determining a plurality of attributes of the plurality of files; and mapping the plurality of files in a positive portion of the coordinate space defined by an intersection of at least two of the plurality of attributes.
 14. A computer-implemented method as in claim 11, further comprising: determining whether any of the individual geometric regions include a radius greater than a threshold value; reducing the radius of the individual geometric regions which are greater than the threshold value such that the radius is less than or equal to the threshold value; and re-defining, after the reducing, the individual geometric regions which no longer include all files from a respective cluster of the plurality of clusters, wherein the re-defining includes defining multiple smaller geometric regions in place of the individual geometric regions.
 15. A computer-implemented method as in claim 11, wherein the one or more first geometric regions include a circular geometry having a center point and a radius, and wherein the file is determined to be located within the one or more first geometric regions when a distance between the center point and a location of the file is less than or equal to the radius.
 16. A computer-implemented method as in claim 15, wherein the center point is determined based on averaging locations for each of the plurality of files located within the one or more first geometric regions.
 17. A computer-implemented method as in claim 15, wherein the center point is determined based on shared attributes for each of the plurality of files located within the one or more first geometric regions.
 18. A computer-implemented method as in claim 15, wherein the radius is determined based on a maximum Euclidian distance between each of the plurality of files located within the one or more first geometric regions.
 19. A method as in claim 11, wherein the classifying employs at least one machine learning model.
 20. A system, comprising: at least one processor; and at least one memory including program code which when executed by the at least one memory provides operations comprising: searching for one or more clusters among a plurality of files in a coordinate space; defining one or more geometric regions around the one or more clusters, the one or more geometric regions classifying a set of files within the one or more geometric regions as belonging to a specific classification type, the one or more geometric regions including a circular geometry having a center point and a radius; determining whether the file is located within the one or more geometric regions based on whether a distance between the center point and a location of the file is less than or equal to the radius; classifying a file as belonging to the specific classification type when the file is located within the one or more geometric regions; and preventing files classified to certain classification types from executing, opening, continuing to execute, writing, or being downloaded.
 21. A system as in claim 20, wherein the specific classification type includes one or more of safe, suspect, benign, unsafe, malicious, non-malicious, downloader, Trojan, and adware.
 22. A system as in claim 20, wherein the operations further comprise: determining a plurality of attributes of the plurality of files; and mapping the plurality of files in the coordinate space, wherein the coordinate space is defined by an intersection of at least two of the plurality of attributes.
 23. A system as in claim 20, wherein the center point is determined based on averaging locations for each of the plurality of files located within the one or more first geometric regions.
 24. A system as in claim 20, wherein the center point is determined based on shared attributes for each of the plurality of files located within the one or more geometric regions.
 25. A system as in claim 20, wherein the radius is determined based on a maximum Euclidian distance between each of the plurality of files located within the one or more geometric regions.
 26. A system as in claim 20, wherein the classifying employs at least one machine learning model.
 27. A system as in claim 20, wherein the classifying employs at least one of: a neural networks, a support vector machine, a logistic regression model, a Bayesian algorithm, or a decision tree.
 28. A method, comprising: searching for one or more clusters among a plurality of files in a coordinate space; defining one or more geometric regions around the one or more clusters, the one or more geometric regions classifying a set of files within the one or more geometric regions as belonging to a specific classification type, the one or more geometric regions including a circular geometry having a center point and a radius; determining whether the file is located within the one or more geometric regions based on whether a distance between the center point and a location of the file is less than or equal to the radius; and classifying a file as belonging to the specific classification type when the file is located within the one or more first geometric regions; and preventing files classified to certain classification types from executing, opening, continuing to execute, writing, or being downloaded.
 29. A method as in claim 28, wherein the specific classification type includes one or more of safe, suspect, benign, unsafe, malicious, non-malicious, downloader, Trojan, and adware.
 30. A method as in claim 28, further comprising: determining a plurality of attributes of the plurality of files; and mapping the plurality of files in the coordinate space, wherein the coordinate space is defined by an intersection of at least two of the plurality of attributes.
 31. A method as in claim 28, wherein the center point is determined based on averaging locations for each of the plurality of files located within the one or more first geometric regions.
 32. A method as in claim 28, wherein the center point is determined based on shared attributes for each of the plurality of files located within the one or more geometric regions.
 33. A method as in claim 28, wherein the radius is determined based on a maximum Euclidian distance between each of the plurality of files located within the one or more geometric regions.
 34. A method as in claim 28, wherein the classifying employs at least one machine learning model. 